class_docstr: Creates patches of cells.
  
  Parameters
  ----------
  base_dataset: CellsDataset
      Dataset of cells
  patch_size: tuple of ints (default=(256, 256))
      The size of each patch
  random_flips: bool (default=False)
      If true, patches and masks will be randomly flipped horizontally and
      vertically.
  padding: int (default=16)
      Amount of paddding around each image and mask
class_docstr_tok: ["Creates","patches","of","cells",".","Parameters","--","--","--","--","--","base_dataset",":","CellsDataset","Dataset","of","cells","patch_size",":","tuple","of","ints","(","default=","(","256",",","256",")",")","The","size","of","each","patch","random_flips",":","bool","(","default=False",")","If","true",",","patches","and","masks","will","be","randomly","flipped","horizontally","and","vertically",".","padding",":","int","(","default=16",")","Amount","of","paddding","around","each","image","and","mask"]
code: def __getitem__(self, idx):
      img_idx, (i, j) = self.coords[idx]
      cell, mask = self.base_dataset[img_idx]
      h, w = self.patch_size_expanded
  
      cell = pad(cell, self.padding, padding_mode="reflect")
      mask = pad(mask, self.padding, padding_mode="reflect")
  
      cell = crop(cell, i, j, h, w)
      mask = crop(mask, i, j, h, w)
  
      if self.random_flips:
          if random.random() < 0.5:
              cell = hflip(cell)
              mask = hflip(mask)
  
          if random.random() < 0.5:
              cell = vflip(cell)
              mask = vflip(mask)
  
      cell = to_tensor(cell)
      mask = torch.as_tensor((np.array(mask) == 255).astype("float32"))
  
      # mean and std of imagenet
      cell = normalize(
          cell, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]
      )
  
      return cell, mask
  
file_path: skorch/examples/nuclei_image_segmentation/dataset.py
imports: from itertools import zip_longest, product, chain
  import random
  import numpy
  import torch
  from torch.utils.data import Dataset
  from torchvision.transforms.functional import pad, to_tensor, normalize, hflip, vflip, crop
  from PIL import Image
label_desc: Use image node_id 3 for calling the PatchedDataset obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: cell,  mask